The Five AI Basics Every Business Executive Needs To Understand Right Now

Data comes before AI: Understand the difference between training and inference.

Training an AI model can be likened to a child learning a language. Most children learn language from countless hours of listening to their parents and everyone else around them talk. They consume massive amounts of “data” over time and gradually learn the language. Depending on the time, the inputs, and the child’s effort and aptitude, certain language proficiencies are achieved.

Like a child, the AI model needs an objective. Then, it is developed by exposing the model to training data that reinforces the objective. With the right type of data and volume of data, (potentially “Big Data”), a sufficient amount of time and processing power, a certain level of proficiency (accuracy) can be achieved. Only then, like a child who learned how to speak and understand a particular language can they then utilize this knowledge to act and play. Then the AI model can be used as part of a business capability or process.

Once trained, an AI model and the capabilities that it powers, can then be used in production, i.e. leveraged as part of a new or better product or service (i.e. think Alexa or Siri), a better customer interaction (think chatbots), or a new or better way of doing business (think internet of things and predictive maintenance). This is called “inference." There are numerous pre-trained models and systems that can be leveraged, acquired, or purchased. And if you can find a model that specifically meets your needs, the training phase is already completed. So, if you purchase a self-driving car, that car is constantly using “inference” to make driving decisions and take actions. But, someone else trained that AI system to do that.

The challenge for a business is that their differentiation may come from combining data from external sources with data which is owned by the business itself. In addition, the objective associated with the business value that the business can create may be different from that of a pre-trained system. There is active research around “transfer learning”, i.e. applying the training performed in one scenario to a different scenario. However, regardless of whether transfer learning can be applied, a business is usually creating unique products and services, customer interactions, and ways of doing business, and possesses unique data. So, “training” is required before “inference” and it all starts with data.

AI is about Statistical Relationships, not Functional Relationships, Executives need to understand Accuracy to make business decisions around AI

As the story goes, when Newton observed an apple falling from a tree, he developed a functional relationship between force, mass, and acceleration due to gravity. With this relationship (F=ma), many things can be “determined” from that equation. AI does not work that way. In this scenario, with AI, thousands or millions of different types of things would be observed falling from different types of things and from different heights. Based on “seeing” all that happens (being trained) the AI system could then make predictions (inference) about how other things will fall. The AI system is statistical. Specifically, it will get some things right and some things wrong. The question that follows is how “accurate” is the AI system in making that prediction?

In business systems, the more important question is how accurate does the AI system need to be? In the current state of AI, it seems, that with more and more data and computing power, training an AI system can become more accurate. But, more and more data and computing power, and more specialized skills to “wrangle” that data comes at greater cost. So, a business executive needs to understand that not only does the objective need to be clearly identified, and the data needed to train to that objective identified, the required accuracy needs to be defined.

For instance, a business creating a fun system based on AI (i.e. recognizing dogs and cats) may not need to be that accurate. However, a system used by a veterinarian to determine a symptom in a dog should be more accurate. A self-driving car that can determine the difference between a dog and or some leaves in the road, needs to be exceedingly accurate. The accuracy required for a specific business system directly relates to data, training time, effort and computing power needed and so is a critical part of business cases.

The math is not rocket science, the scale is. Business executives need to understand that AI success is more and more about the AI infrastructure used to innovate.

As discussed above, AI needs to learn (be trained). There is a lot of research going on around learning from smaller data sets. This is because successful AI today provides required accuracies by training on very large datasets. And until this research bears fruit, successful AI training takes lots and lots of data and to train in reasonable time periods and takes significant compute resources.

While current AI bottlenecks tend to focus on lack of skilled “Data Scientists”, more and more students are taking courses around Machine Learning (the data science part of AI) and even more are taking the MOOCs around AI like the free course fast.ai or Googles free crash course on Machine Learning. There is also NVIDIA's Deep Learning Institute which offers online courses that give you access to a fully configured GPU-accelerated workstation in the cloud, complete with software tools, neural networks, and datasets.

In addition, more and better tools are coming out to create AI models, like Google’s TensorFlow and a lot of work is being put into AI platforms to automate much of the “data munging” and “knob turning” that Data Scientists need to perform. So, AI is becoming less about Rocket Science and more doable and reachable for more and more businesses. The keys that are not going away right now are the need for data and the need for infrastructure to train on that data. So, business executives need to understand how they will have this Big Data/AI infrastructure or platform to leverage so that AI innovation is realized within their business.

The future is uncertain, but AI use case opportunities for business are clear today. Business executives need to understand the AI opportunities and challenges specific to their business without being distracted.

There are a lot of scary doomsday scenarios associated with talk of robots and Skynet around AI. This talk leads to Executives being distracted about the very real challenges and opportunities AI presents today.

First, about the “scary” future. Today, the largest AI Machine Learning “Deep Neural Nets” have tens of millions of neurons. The latest research on a human brain says we have about eighty six billion neurons with one hundred and fifty trillion connections. AI systems are just not there yet. In twenty or fifty years, that may change. A lot of researchers also think the current state of the way AI is trained will reach a brick wall. i.e. the research postulated that the way we are doing AI today is not the way the brain works. So not only are we at orders of magnitude less in scale and having trouble at that scale, the brain probably does things much different and better than the way we currently train AI networks.

For business executives looking to improve customer interactions, provide new ways of doing business and delivering new products and services, AI is believed to be a must have. Today, there are real opportunities and challenges for which business executives can leverage AI. AI is getting better and better at computer vision (i.e. used for self-driving cars), natural language processing (i.e. for language translation or processing unstructured text), and speech recognition (i.e. think Siri or Alexa). Business needs to innovate and AI offers that innovation opportunity that they or their competitors will leverage. They need to understand what they can detect, classify, segment, predict, or recommend that will allow them to create new ways of doing business, new customer interactions, and new or improved products and services.

In addition, they need to understand the real risks about using AI today. That risk is centered around the quality of training data. Is that data faulty, biased, have errors or have other problems? A model built with such data will then be faulty, biased, error prone, etc… Business Executives need to take training data very seriously. If they are leveraging pre-trained models, how do they know what that model was trained on? If they are training AI, is the data they are using proper? It is one thing to misidentify a dog or a cat because of bad training data, but a completely different thing to treat a customer improperly because of improper data. Proper data control must be implemented for all AI initiatives.

If AI is to put Machine Learning and data to use for business benefit, Business Executives need to be directly and actively involved.

Right now, too often, whether it is because of time, lack of understanding, or not believing that AI is applicable to their business, most executives are not actively involved in AI initiatives within their business. They “outsource” this work to technical teams. Business Executives responsibilities are specifically around the success of their business. AI is not about technical analysis, it is about leveraging data and machine learning to drive business success. Without business leadership, AI success in business will only be random and limited. Active and indeed proactive involvement of business leadership is critical. And that is why business executives need to understand the 5 things covered in this article about AI right now.

Lucd.ai is one of 2,800+ AI startups in NVIDIA's Inception program. The virtual accelerator program provides startups with access to technology, expertise and marketing support.